Implications for sequencing of biologic therapy and choice of second anti-TNF in patients with inflammatory bowel disease:results from the IMmunogenicity to Second Anti-TNF therapy (IMSAT) therapeutic drug monitoring study

BACKGROUND: Anti-drug antibodies are associated with treatment failure to anti-TNF agents in patients with inflammatory bowel disease (IBD).AIM: To assess whether immunogenicity to a patient's first anti-TNF agent would be associated with immunogenicity to the second, irrespective of drug sequence METHODS: We conducted a UK-wide, multicentre, retrospective cohort study to report rates of immunogenicity and treatment failure of second anti-TNF therapies in 1058 patients with IBD who underwent therapeutic drug monitoring for both infliximab and adalimumab. The primary outcome was immunogenicity to the second anti-TNF agent, defined at any timepoint as an anti-TNF antibody concentration ≥9 AU/ml for infliximab and ≥6 AU/ml for adalimumab.RESULTS: In patients treated with infliximab and then adalimumab, those who developed antibodies to infliximab were more likely to develop antibodies to adalimumab, than patients who did not develop antibodies to infliximab (OR 1.99, 95%CI 1.27-3.20, p = 0.002). Similarly, in patients treated with adalimumab and then infliximab, immunogenicity to adalimumab was associated with subsequent immunogenicity to infliximab (OR 2.63, 95%CI 1.46-4.80, p < 0.001). For each 10-fold increase in anti-infliximab and anti-adalimumab antibody concentration, the odds of subsequently developing antibodies to adalimumab and infliximab increased by 1.73 (95% CI 1.38-2.17, p < 0.001) and 1.99 (95%CI 1.34-2.99, p < 0.001), respectively. Patients who developed immunogenicity with undetectable drug levels to infliximab were more likely to develop immunogenicity with undetectable drug levels to adalimumab (OR 2.37, 95% CI 1.39-4.19, p < 0.001). Commencing an immunomodulator at the time of switching to the second anti-TNF was associated with improved drug persistence in patients with immunogenic, but not pharmacodynamic failure.CONCLUSION: Irrespective of drug sequence, immunogenicity to the first anti-TNF agent was associated with immunogenicity to the second, which was mitigated by the introduction of an immunomodulator in patients with immunogenic, but not pharmacodynamic treatment failure

Implications for sequencing of biologic therapy and choice of second anti-TNF in patients with inflammatory bowel disease:results from the IMmunogenicity to Second Anti-TNF therapy (IMSAT) therapeutic drug monitoring study

BACKGROUND: Anti-drug antibodies are associated with treatment failure to anti-TNF agents in patients with inflammatory bowel disease (IBD).AIM: To assess whether immunogenicity to a patient's first anti-TNF agent would be associated with immunogenicity to the second, irrespective of drug sequence METHODS: We conducted a UK-wide, multicentre, retrospective cohort study to report rates of immunogenicity and treatment failure of second anti-TNF therapies in 1058 patients with IBD who underwent therapeutic drug monitoring for both infliximab and adalimumab. The primary outcome was immunogenicity to the second anti-TNF agent, defined at any timepoint as an anti-TNF antibody concentration ≥9 AU/ml for infliximab and ≥6 AU/ml for adalimumab.RESULTS: In patients treated with infliximab and then adalimumab, those who developed antibodies to infliximab were more likely to develop antibodies to adalimumab, than patients who did not develop antibodies to infliximab (OR 1.99, 95%CI 1.27-3.20, p = 0.002). Similarly, in patients treated with adalimumab and then infliximab, immunogenicity to adalimumab was associated with subsequent immunogenicity to infliximab (OR 2.63, 95%CI 1.46-4.80, p < 0.001). For each 10-fold increase in anti-infliximab and anti-adalimumab antibody concentration, the odds of subsequently developing antibodies to adalimumab and infliximab increased by 1.73 (95% CI 1.38-2.17, p < 0.001) and 1.99 (95%CI 1.34-2.99, p < 0.001), respectively. Patients who developed immunogenicity with undetectable drug levels to infliximab were more likely to develop immunogenicity with undetectable drug levels to adalimumab (OR 2.37, 95% CI 1.39-4.19, p < 0.001). Commencing an immunomodulator at the time of switching to the second anti-TNF was associated with improved drug persistence in patients with immunogenic, but not pharmacodynamic failure.CONCLUSION: Irrespective of drug sequence, immunogenicity to the first anti-TNF agent was associated with immunogenicity to the second, which was mitigated by the introduction of an immunomodulator in patients with immunogenic, but not pharmacodynamic treatment failure

Application of Machine Learning in Detecting Iron Deficiency Anemia Using Conjunctiva image Dataset from Ghana

Anemia is a global public health issue that mostly emerges as a result of a decrease in red blood cell count and is particularly prevalent in Africa. Invasive ways of detecting anemia are expensive and time-consuming. Anemia may, however, be diagnosed using non-invasive technologies such as machine learning algorithms. In our study, we compared machine learning models to detect anemia using the conjunctiva of the eyes. The main datasets consisting of the conjunctiva of the eyes were acquired using Ghana as a case study for dataset collecting.Before the study began, the ethical committees at the hospitals involved approved the collection of datasets. Also, because the participants (patients) in the study were minors, the ethical agreement was obtained from their parent(s) or guardian(s), and the purpose and objectives of the study were explained to them, along with the advantages of the health services. Before the participants were enrolled in the data collection, their parent(s) or guardian(s) gave their consent. Furthermore, the ethics and consent committee of the University of Energy and Natural Resources, Ghana approved the start of this experiment. Furthermore, patients' or participants' names and faces were not shown or exposed during image capture, rendering their identification unknownTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images

Anemia is one of the global public health challenges that particularly affect children and pregnant women. A study by WHO indicates that 42% of children below the age of 6 and 40% of pregnant women worldwide are anemic. This affects the world's total population by 33%, due to the cause of iron deficiency. The non-invasive technique, such as the use of machine learning algorithms is one of the methods used in the diagnosis or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, a machine learning approach was used to detect iron-deficiency anemia with the application of Naïve Bayes, CNN, SVM, k-NN, and decision tree algorithms. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the color of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The method utilized was categorized into three different stages: dataset collection, dataset preprocessing, and model development for anemia detection. The CNN achieved a higher accuracy of 99.12%, while the SVM had the least accuracy of 95.4%. The performance of the models justifies that the non-invasive approach is an effective mechanism for anemia detection

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

Background: Anemia is one of the global public health problems that affect children and pregnant women. Anemia occurs when the level of red blood cells within the body decreases or when the structure of the red blood cells is destroyed or when the Hb level in the red blood cell is below the normal threshold, which results from one or more increased red cell destructions, blood loss, defective cell production or a depleted sum of Red Blood Cells. Methods: The method used in this study is divided into three phases: the datasets were gathered, which is the palm, pre-processed the image, which comprised; Extracted images, and augmented images, segmented the Region of Interest of the images and acquired their various components of the CIE L*a*b* colour space (also referred to as the CIELAB), and finally developed the proposed models for the detection of anemia using the various algorithms, which include CNN, k-NN, Nave Bayes, SVM, and Decision Tree. The experiment utilized 527 initial datasets, rotation, flipping and translation were utilized and augmented the dataset to 2635. We randomly divided the augmented dataset into 70%, 10%, and 20% and trained, validated and tested the models respectively. Results: The results of the study justify that the models performed appropriately when the palm is used to detect anemia, with the Naïve Bayes achieving a 99.96% accuracy while the SVM achieved the lowest accuracy of 96.34%, as the CNN also performed better with an accuracy of 99.92% in detecting anemia. Conclusions: The invasive method of detecting anemia is expensive and time-consuming; however, anemia can be detected through the use of non-invasive methods such as machine learning algorithms which is efficient, cost-effective and takes less time. In this work, we compared machine learning models such as CNN, k-NN, Decision Tree, Naïve Bayes, and SVM to detect anemia using images of the palm. Finally, the study supports other similar studies on the potency of the Machine Learning Algorithm as a non-invasive method in detecting iron deficiency anemia

Implications for sequencing of biologic therapy and choice of second anti-TNF in patients with inflammatory bowel disease: Results from the IMmunogenicity to Second Anti-TNF therapy (IMSAT) therapeutic drug monitoring study.

BACKGROUND: Anti-drug antibodies are associated with treatment failure to anti-TNF agents in patients with inflammatory bowel disease (IBD). AIM: To assess whether immunogenicity to a patient's first anti-TNF agent would be associated with immunogenicity to their second, irrespective of drug sequence METHODS: We conducted a UK-wide, multicentre, retrospective cohort study to report rates of immunogenicity and treatment failure of second anti-TNF therapies in 1058 patients with IBD who underwent therapeutic drug monitoring for both infliximab and adalimumab. The primary outcome was immunogenicity to the second anti-TNF drug, defined at any timepoint as an anti-TNF antibody concentration ≥9 AU/ml for infliximab and ≥6 AU/ml for adalimumab. RESULTS: In patients treated with infliximab and then adalimumab, those who developed antibodies to infliximab were more likely to develop antibodies to adalimumab, than patients who did not develop antibodies to infliximab (OR 1.99, 95%CI 1.27-3.20, p = 0.002). Similarly, in patients treated with adalimumab and then infliximab, immunogenicity to adalimumab was associated with subsequent immunogenicity to infliximab (OR 2.63, 95%CI 1.46-4.80, p < 0.001). For each 10-fold increase in anti-infliximab and anti-adalimumab antibody concentration, the odds of subsequently developing antibodies to adalimumab and infliximab increased by 1.73 (95% CI 1.38-2.17, p < 0.001) and 1.99 (95%CI 1.34-2.99, p < 0.001), respectively. Patients who developed immunogenicity with undetectable drug levels to infliximab were more likely to develop immunogenicity with undetectable drug levels to adalimumab (OR 2.37, 95% CI 1.39-4.19, p < 0.001). Commencing an immunomodulator at the time of switching to the second anti-TNF was associated with improved drug persistence in patients with immunogenic, but not pharmacodynamic failure. CONCLUSION: Irrespective of drug sequence, immunogenicity to the first anti-TNF agent was associated with immunogenicity to the second anti-TNF, which was mitigated by the introduction of an immunomodulator in patients with immunogenic, but not pharmacodynamic treatment failure